From Data to Action: Accelerating Investigative Workflows and Fighting Fraud with Advanced Analytics
The U.S. federal government has more than 70 inspectors general (IGs) spread across dozens of agencies. Most states have at least one inspector general, while many states have five to 10 IGs. Florida holds the top rank with more than 40.
IGs play a critical role in investigating and preventing unlawful operations in their agencies. However, with the exponential growth of government benefit programs and the increasing sophistication of fraudsters, IGs are facing unprecedented challenges in their efforts to detect and prevent fraud.
The COVID-19 pandemic and the subsequent implementation of government lending and benefit programs to mitigate economic damage created unprecedented opportunities for fraud. Governments, in their haste to distribute funds and streamline application processes, inadvertently opened the door for fraudulent activities. In the years since, investigators have been grappling with the immense task of identifying and prosecuting fraudsters, a testament to the challenges faced by IGs in their fight against fraud.
The U.S. federal Small Business Administration (SBA) was one of the agencies that distributed pandemic-related funds. Its Inspector General recently ranked its economic relief programs as susceptible to significant fraud risks and vulnerabilities and called that “Challenge Number One.”
As a testament to the effectiveness of advanced analytic technologies, more IGs are following the lead of private-sector banks and insurance companies. The U.S. Treasury Department is a prime example. In February 2024, it announced the recovery of over $375 million in fraudulent payment losses, thanks to the implementation of an enhanced fraud detection data analytics process utilizing Artificial Intelligence (AI).
The Treasury Department said they were “committed to ensuring that Social Security payments, tax refunds, and other types of checks, and people receiving them, are safe from fraud. We are using the latest technological advances to enhance our fraud detection process. AI has allowed us to expedite fraud detection and recovery of tax dollars,” according to Deputy Secretary of the Treasury Wally Adeyemo.
No federal agency distributes more money directly to U.S. citizens than the Social Security Administration (SSA), and its IG recently reported on SSA’s commitment to applying AI in its fraud prevention efforts – and, ironically, reported that fraudsters were also using AI to steal money from Social Security recipients.
SSA investigators discovered that criminals were using a “chatbot” to impersonate beneficiaries, contact SSA customer service representatives, change SSA beneficiaries’ direct deposit account information, and divert their monthly benefit payments to spurious accounts, SSA Inspector General Gail S. Ennis revealed in a letter to Congress. “Like the impersonation scams OIG investigates, the chatbot numbers originated overseas. The chatbots effectively moved stolen Social Security benefits into the stream of criminal commerce here in the United States, where organized rings of ‘money mules’ collected and moved the proceeds,” Ennis wrote.
How can advanced technology like decision intelligence platforms help IGs improve their effectiveness in finding and fighting fraud?
As fraud schemes grow more sophisticated, investigative agencies must improve, too. The key isn’t just having access to more technology — it’s knowing how to harness data effectively. By following proven fraud analytics practices, agencies can move from simply reacting to fraud to detecting and preventing it.
Data Analytics Fundamentals for Fraud
Advanced fraud detection isn’t just about having better tools — it’s about using data in smarter, more strategic ways. Agencies adopting decision intelligence platforms are seeing success by applying a few core principles that guide how they detect and prevent fraud.
1. Comprehensive, reliable data
Fraud detection begins with complete, accurate data. Investigators must bring together information from a wide range of sources to create a full, trustworthy picture. Advanced decision intelligence platforms make it easier to merge, structure, and explore these varied datasets.
2. Identify patterns and irregularities
Analytics tools help define typical behavior and spot when something doesn’t fit. AI enhances this process by continuously learning from new data, adapting to emerging fraud methods over time.
3. Prioritizing high-risk cases
Given the limited resources, not every anomaly deserves equal attention. Risk-scoring models help investigative teams rank threats by severity, allowing them to focus first on the most damaging risks.
4. Ongoing monitoring and rapid response
Rather than relying only on periodic reviews, agencies increasingly monitor transactions and activities in real time. Immediate alerts allow investigators to act faster, minimizing potential losses.
5. Clear, defensible results
Detection systems must not only catch suspicious activity but also clearly explain why certain cases were flagged. Transparent, explainable analytics are essential for building strong cases and maintaining public confidence.
6. Dealing with new threats
Fraudsters constantly change their tactics. Successful analytics systems must be flexible — regularly updating models and adapting detection strategies based on new fraud patterns and behaviors.
Fraud Detection Techniques and Algorithms
Fighting fraud requires more than traditional investigation methods. Today’s most effective strategies combine advanced analytics, machine learning and behavioral insights to spot suspicious activity faster and more accurately. Investigators use a range of specialized techniques to detect hidden threats and improve response time.
Key techniques include:
Anomaly detection
Anomaly detection focuses on identifying activities or behaviors that fall outside the expected norm. In fraud investigations, this could mean flagging transactions that are unusually large, account access from unfamiliar locations, or sudden changes in user behavior. By spotting these irregularities early, investigators can intervene before serious damage occurs.
Predictive analytics
Predictive models help investigators assess the likelihood of fraud based on past behavior. Using historical data, algorithms generate risk scores and predictions about future fraudulent activity. As more data is fed into the system, these models adapt and refine their accuracy — allowing teams to prioritize cases more effectively.
Pattern recognition and link analysis
Fraudsters often repeat successful tactics. Pattern recognition algorithms detect these repeated strategies, while link analysis maps connections between people, organizations, and transactions. Together, these techniques reveal hidden networks and expose the structures behind coordinated fraud operations.
Machine learning approaches
Machine learning plays a role in fraud analytics. Supervised learning trains algorithms using labeled examples of fraud versus legitimate activity, while unsupervised learning uncovers new and unknown fraud patterns. This dual approach helps investigators discover both known risks and emerging threats.
Behavioral analytics
Behavioral analytics track how users interact with systems over time. Changes in typical behavior — such as unusual transaction patterns, device switching, or repeated failed login attempts — can signal potential fraud. Monitoring behavior adds an important layer of context beyond simple transaction data.
Big Data analysis
Modern fraudsters operate across platforms, making it essential to analyze massive, varied datasets. Big data tools allow investigators to aggregate information from financial systems, communication channels, the dark web, and other sources to build a comprehensive picture of fraud activities and accelerate detection.
Data fusion
One fundamental advantage of decision intelligence technology is its ability to combine huge volumes of data and diverse types and sources of data and fuse them into a single platform and workspace for fast, easy analysis, visualization and sharing. This allows investigation teams to accelerate their discovery and decision-making. Today’s technology enables faster fraud data analysis, helping investigators to quickly identify relevant patterns, profiles and connections, regardless of data formats and languages.
AI-driven risk scoring
Decision intelligence platforms like NEXYTE enable investigators to discover more illicit and fraudulent transactions. The tools’ AI capabilities enable fraud risk analytics models to rapidly improve their effectiveness as they process more and more data, learning as they go. The platform can also be used to tag and correlate any form of customer’s data such as images, financial records, Dark Web interactions, blockchain transfers, encrypted communications, social media posts and more, in order to discover previously undetectable insights, uncover hidden relations, and highlight suspicious entities or transactions.
Information sharing
Many of the most sophisticated fraud rings operate internationally in multiple countries and, unsurprisingly, use various tactics to mask their true locations and identities. So, these investigations often require collaboration between law enforcement agencies in multiple jurisdictions. Tools like NEXYTE facilitate this type of collaboration between teams with multi-layered access control and a fine-grained permissions model to handle sensitive financial data, allowing agencies to leverage the expertise of multiple domains within a law enforcement or investigative organization.
This is how SSA IG Gail S. Ennis summed things up: “AI will be a powerful tool to support the federal government’s ability to detect and prevent the fraudulent disbursement of taxpayers’ dollars.”
It’s good to see more and more public sector leaders in agreement on this view – and taking action to put decision intelligence tools to work.
Learn more about how NEXYTE can streamline your investigative processes and enhance fraud detection here.